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Innovations in Neural Information Paradigms and Applications

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Cover of 'Innovations in Neural Information Paradigms and Applications'

Table of Contents

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    Book Overview
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    Chapter 1 Innovations in Neural Information Paradigms and Applications
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    Chapter 2 Self-Organizing Maps for Structured Domains: Theory, Models, and Learning of Kernels
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    Chapter 3 Unsupervised and Supervised Learning of Graph Domains
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    Chapter 4 Neural Grammar Networks
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    Chapter 5 Estimates of Model Complexity in Neural-Network Learning
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    Chapter 6 Regularization and Suboptimal Solutions in Learning from Data
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    Chapter 7 Probabilistic Interpretation of Neural Networks for the Classification of Vectors, Sequences and Graphs
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    Chapter 8 Metric Learning for Prototype-Based Classification
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    Chapter 9 Bayesian Linear Combination of Neural Networks
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    Chapter 10 Credit Card Transactions, Fraud Detection, and Machine Learning: Modelling Time with LSTM Recurrent Neural Networks
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    Chapter 11 Towards Computational Modelling of Neural Multimodal Integration Based on the Superior Colliculus Concept
Attention for Chapter 10: Credit Card Transactions, Fraud Detection, and Machine Learning: Modelling Time with LSTM Recurrent Neural Networks
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